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Record W2577744696 · doi:10.1177/0886260516683173

Anxiety Sensitivity Mediates Relations Between Attachment and Aggression Differently by Gender

2016· article· en· W2577744696 on OpenAlexaff
Margo C. Watt, Catherine E. Gallagher, Marie‐Eve Couture, Robyn Wells, Kim MacLean

Bibliographic record

VenueJournal of Interpersonal Violence · 2016
Typearticle
Languageen
FieldPsychology
TopicAttachment and Relationship Dynamics
Canadian institutionsSt. Francis Xavier UniversityDalhousie University
Fundersnot available
KeywordsAggressionPsychologyAnxietyDevelopmental psychologyAnxiety sensitivityPoison controlAttachment theoryClinical psychologyMedicinePsychiatryMedical emergency

Abstract

fetched live from OpenAlex

The present study examined relations among attachment, aggression, and anxiety sensitivity (AS) in a sample of male and female undergraduates. Given that some individuals may use aggression to modulate negative emotional states, it was predicted that AS dimensions would mediate relations between attachment anxiety (vs. attachment avoidance) and certain forms of aggression, particularly impulsive aggression. Moreover, it was hypothesized that the relations among attachment, aggression, and AS would be moderated by gender. Participants ( N = 1,042) completed measures of attachment (Experiences in Close Relationships–Revised [ECR-R]), aggression (Aggression Questionnaire [AQ]; Impulsive/Premeditated Aggression Scales [IPAS]), and AS (AS Index–3 [ASI-3]). Results indicated that AS mediated relations between attachment dimensions (both anxiety and avoidance) and most forms of aggression, with each of the AS dimensions playing a unique role differentially by gender. Cognitive concerns emerged as a significant mediator, particularly for men; physical and social concerns played more of a mediating role for women. Interestingly, none of the AS dimensions played a significant mediating role between attachment (either anxiety or avoidance) and physical aggression for men. Results are discussed in terms of their clinical implications and directions for future research.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.040
Threshold uncertainty score0.340

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.021
GPT teacher head0.350
Teacher spread0.329 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations8
Published2016
Admission routes1
Has abstractyes

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